Recent growing interest in ambient intelligent environments has driven a desire for effective models to reason about activities of multiple residents. Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this research, however current works tend to focus on developing statistical algorithms for prediction, whilst there still lacks a study to fully understand the relations of residents’ behaviours and how they are reflected through the sensors’ states. In this paper we investigate the dependencies of the activities from residents and their interaction with the environments. We represent such dependencies in Bayesian networks that leads to construction of six variants of Hidden Markov Models (HMMs). Furthermore, we argue that a complete model should embody more than one type of dependency. Therefore, we propose an ensemble of HMMs, and then generalize it to a novel mixed-dependency model. In the experiments we perform intensive evaluation of our study on multi-resident activity recognition task. The results show that the proposed models outperform other models in three smart home environments, thus asserting our hypothesis.

Item Type:	Refereed Article
Keywords:	smart homes, multi-resident activity recognition, Hidden Markov Models, activity recognition, ambient sensors, aged care
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Intelligent robotics
Objective Division:	Health
Objective Group:	Specific population health (excl. Indigenous health)
Objective Field:	Health related to ageing
UTAS Author:	Tran, SN (Dr Son Tran)
ID Code:	140696
Year Published:	2020
Web of Science® Times Cited:	6
Deposited By:	Information and Communication Technology
Deposited On:	2020-09-01
Last Modified:	2020-10-23
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